Note: Skyline is still under active development and should be considered an "alpha" product. Its usage and system requirements are subject to change between versions. See Versioning for more details.
Skyline works with GPU-based neural networks that are implemented in PyTorch. To run Skyline, you need:
Skyline is currently only supported on Ubuntu 18.04. It should also work on other Ubuntu versions that can run Atom and that have Python 3.6+.
Skyline consists of two components: a command line tool and an Atom plugin
(this repository). Both components must be installed to use Skyline. They can
be installed using
pip install skyline-cli apm install skyline
After installing Skyline, you will be able to invoke the command line tool by
skyline in your shell.
To use Skyline in your project, you need to first write an entry point file, which is a regular Python file that describes how your model is created and trained. See the Entry Point section for more information.
Once your entry point file is ready, navigate to your project's root directory and run:
skyline interactive path/to/entry/point/file
Then, open up Atom, execute the
Skyline:Toggle command in the command palette
(Ctrl-Shift-P), and hit the "Connect" button that appears on the right.
To shutdown Skyline, just execute the
Skyline:Toggle command again in the
command palette. You can shutdown the interactive profiling session on the
command line by hitting Ctrl-C in your terminal.
You can also toggle the Skyline through the Atom menus: Packages > Skyline > Show/Hide Skyline.
Important: To analyze your model, Skyline will actually run your code. This
means that when you invoke
skyline interactive, you need to make sure that
your shell has the proper environments activated (if needed). For example if
virtualenv to manage your model's dependencies, you need to activate
virtualenv before starting Skyline.
Usage Statistics: Skyline collects usage statistics in order to help us make improvements to the tool. If you do not want Skyline to collect usage statistics, you can disable this functionality through Skyline's package settings in Atom (Atom > Settings/Preferences > Packages > Skyline > Settings).
To use Skyline, all of the code that you want to profile interactively must be stored under one common directory. Generally, this just means you need to keep your own source code under one common directory. Skyline considers all the files inside this common directory to be part of a project, and calls this common directory your project's root directory.
When starting a Skyline interactive profiling session, you must invoke
skyline interactive <entry point> inside your project's root directory.
Skyline uses an entry point file to learn how to create and train your model. An entry point file is a regular Python file that contains three top-level functions:
These three functions are called providers and must be defined with specific signatures. The easiest way to understand how to write the providers is to read through an example.
Suppose that your project code is kept under a
my_project ├── __init__.py └── model.py
and your model is defined in
import torch.nn as nnclass Model(nn.Module):def __init__(self):super().__init__()self.conv = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3)self.linear = nn.Linear(in_features=387096, out_features=10)def forward(self, input):out = self.conv(input)return self.linear(out.view(-1, 387096))
One way to write the entry point file would be:
import torchimport torch.nn as nnfrom my_project.model import Modelclass ModelWithLoss(nn.Module):def __init__(self):super().__init__()self.model = Model()self.loss_fn = nn.CrossEntropyLoss()def forward(self, input, target):output = self.model(input)return self.loss_fn(output, target)def skyline_model_provider():# Return a GPU-based instance of our model (that returns a loss)return ModelWithLoss().cuda()def skyline_input_provider(batch_size=32):# Return GPU-based inputs for our modelreturn (torch.randn((batch_size, 3, 256, 256)).cuda(),torch.randint(low=0, high=9, size=(batch_size,)).cuda(),)def skyline_iteration_provider(model):# Return a function that executes one training iterationoptimizer = torch.optim.SGD(model.parameters(), lr=1e-3)def iteration(*inputs):optimizer.zero_grad()out = model(*inputs)out.backward()optimizer.step()return iteration
One important thing to highlight is our use of a wrapper
module. Since Skyline needs to be able to call
.backwards() directly on the
output tensor of our model, we need to use this wrapper module to compute and
return the loss of our model's output with respect to the targets (i.e. the
labels). We also include the targets as inputs to our wrapped module and in our
You can place these provider functions either in a new file or directly in
model.py. Whichever file contains the providers will be your project's entry
point file. In this example, suppose that we defined the providers in a
separate file called
my_project is in your home directory. To launch Skyline you
would run (in your shell):
cd ~/my_project skyline interactive entry_point.py
def skyline_model_provider() -> torch.nn.Module:pass
The model provider must take no arguments and return an instance of your model
torch.nn.Module) that is on the GPU (i.e. you need to call
the module before returning it).
Important: Your model must return a tensor on which
.backward() can be
called. Generally this means that the
torch.nn.Module you return must compute
the loss with respect to the inputs passed into the model.
def skyline_input_provider(batch_size: int = 32) -> Tuple:pass
The input provider must take a single
batch_size argument that has a default
value (the batch size you want to profile with). It must return an iterable
(does not have to be a
tuple) that contains the arguments that you would
normally pass to your model's
forward method. Any
Tensors in the returned
iterable must be on the GPU (i.e. you need to call
.cuda() on them before
def skyline_iteration_provider(model: torch.nn.Module) -> Callable:pass
The iteration provider must take a single
model argument, which will be an
instance of your model. This provider must return a callable (e.g., a function)
that, when invoked, runs a single training iteration.
Skyline uses semantic versioning. Before the 1.0.0 release, backwards compatibility between minor versions will not be guaranteed.
The Skyline command line tool and plugin use independent version numbers. However, it is very likely that minor and major versions of the command line tool and plugin will be released together (and hence share major/minor version numbers).
Generally speaking, the most recent version of the command line tool and plugin will be compatible with each other.
Geoffrey Yu email@example.com
Good catch. Let us know what about this package looks wrong to you, and we'll investigate right away.